Comparison between Regressive and Classifying Neural Networks for PID Controlled Path-Following

In this paper, a regressive CNN-based system is developed with the objective of overcoming the turning angle limitations of a previously developed path-following classifying neural network. Images from a forward facing camera are put through a CNN and the result is supplied to a PID controller to guide the robot down a path. Trials were run for both current and previous methods and their results are compared and contrasted, with the new method completing all courses. Our regressive CNN was determined to have a RMSE of 3.584 and a R² of 0.987.